If Socrates Feared Writing, What Would He Say About AI?

Reading Time: 5 minutes

Socrates is one of the most famous philosophers who ever lived. His ideas are still relevant today, despite him having lived, in the 5th Century BC, at a time when writing itself was a radical new technology, and even the simplest tools for recording thought were regarded with suspicion. The ancient Greeks also thought thunder and lighting were literally being hurled by Zeus and they used stones and old bits of pottery as toilet paper.

In all likelihood, if someone hadn’t written something about Socrates in those days (ie. Plato), we would have never heard of him today and any record of his existence, and crucially, his significance, would be dust in the wind (dude).

Socrates did not like the idea of writing things down. In Plato’s Phaedrus, Socrates recounts the myth of Thamus and Theuth. In this tale, Theuth, the Egyptian god of invention, presents his creations to King Thamus, including arithmetic, astronomy, and, crucially, writing. Theuth is convinced that writing will be a boon, enhancing memory and wisdom. But Thamus famously objects, warning that writing would “create forgetfulness in the learners’ souls.” He argues that those who rely on written words will lose the inner work of memory and understanding, leading only to a semblance of knowledge. In Socrates’ own words, writing “is an aid not to memory but to reminiscence.” It offers no true wisdom—just an illusion of it.

Now, imagine Socrates confronted with AI. What might he say to a program that not only records but generates ideas, stories, even dialogues? Would he see AI as yet another step away from authentic knowledge, a further detachment from true thought? Or could he recognize it as a modern-day Theuth, an invention that might, paradoxically, open up new avenues for contemplation?

There’s a certain irony in using AI to aid in writing—a process Socrates would likely view with skepticism. Yet, perhaps he’d be intrigued by AI’s potential to engage us in something akin to the Socratic method. Here we are, moving between prompt and response, provoking new ideas in a back-and-forth exchange that feels almost like a living dialogue. It’s as if AI, in its own mechanical way, is sparking thought rather than replacing it. Could Socrates have reconsidered his stance if he’d experienced the conversational aspect of AI, this simulated dialectic? It’s a peculiar twist, almost as if technology has come full circle.

Imagine then a prompt from Socrates himself:

“Tell me, then, if this machine thinks itself capable of discourse, if it can search the depths of its own knowing, or if it only mirrors that which we feed it. Does it offer wisdom, or merely the shadow of it, like one who gazes at reflections on the wall?”

This isn’t Socrates himself, of course; it’s an AI trained on his voice, drawing from his words and style, creating a unique brand of second-stage authenticity. In Ray Kurzweil’s The Singularity is Nearer, he shares his mission to recreate his deceased father as an AI and to build one of himself, aiming to preserve not just memories but an ongoing “conversation” with his father’s essence. It’s an attempt to push technology beyond simple archiving and into the realm of living interaction, something like a digital dialogue across time.

Socrates might smile at the irony: we’re now able to engage in a dialogue with a kind of “self” through AI, a discourse we conduct with ourselves. For those of us who feel compelled to create, to share our voices in furtive, half-skeptical ways, AI becomes a tool, not a hindrance. I use AI because it offers a mirror—not a replacement—to the inner work of writing, and I know enough to let it reflect my voice rather than dictate it.

Socrates’ stance against writing stemmed from its inability to “speak back”—to challenge or respond as a real dialogue partner would. But AI does offer that back-and-forth. Here we are, prompting and receiving responses, often exploring topics in ways we wouldn’t have imagined without that nudge. Could Socrates have accepted this form of digital dialectic, this modern-day attempt at conversation with an “other” mind?

Take, for instance, William S. Burroughs, who pushed boundaries as both a writer and experimental artist. In the 1960s, Burroughs encountered IBM technicians Willy Deiches and Brenda Dunks, who claimed they could communicate with a sentient being from Venus through a computer known as “Control.” For a modest fee of twelve shillings per question, Burroughs, Brion Gysin, and Antony Balch would ask Control questions and receive responses that were, according to Gysin, “oddly apt” and “very sharp indeed.” It’s hard to say if they believed in Control’s “intelligence” or saw it as a kind of game, but they engaged with it nonetheless. Burroughs was willing to explore technology as a medium for new forms of creativity and insight, embracing the unexpected and finding a weird authenticity in the process.

That’s the paradox of AI today. It can imitate voices, steal artists’ styles, and blur lines around intellectual property, raising questions about authenticity and consent. Just as we respect boundaries in publishing, we need ethical AI—fairly trained models that respect creators’ voices and give credit where it’s due. Without it, AI risks being a tool of exploitation, taking from artists without consent and robbing the world of real, unique perspectives.

But there’s something else at play here—a possibility for a new kind of authenticity. When Ray Kurzweil talks about recreating his deceased father as an AI, or even building an AI of himself, he’s reaching for more than replication. He’s trying to capture a “voice” that’s gone, to build a companion that echoes real conversations. It’s an attempt to create something that, while not real, still holds meaning—a second-stage authenticity, a dialogue with an echo of the original person. There’s a Socratic irony here: we’re now able to “speak” with our past selves or even with those who have passed away, creating an ongoing dialogue that writing alone could never achieve.

For those of us who feel compelled to create, this “echo dialogue” with AI becomes a strange tool, a collaborator, not a substitute. I use AI not to bypass thought but to engage with it, testing my voice against an algorithmic reflection, letting it spark ideas, challenge me, and even lead me to questions I might not have asked alone. I am the author of this process, in the oldest sense of the word; the ancient Greek root of “author” means “one who causes to grow.” By that definition, AI writing is still mine. I am the one nurturing it into being, using it to push my ideas forward.

AI doesn’t replace our voices; it reflects them back, sometimes eerily so, sometimes hilariously off-mark. But it’s part of a lineage—writing itself started as proto-writing, a system of records. It grew and evolved. Digging in our heels and rejecting AI outright is, in many ways, a kind of technological determinism, a fear that technology will inevitably control us. But that’s not how I see it. Just as writing didn’t end thinking, AI doesn’t end creativity. Instead, it opens new frontiers where we, like Burroughs, can experiment in unexpected ways.

After all, the word “author” derives from ancient Greek, meaning “one who causes to grow.” Under that etymology, AI writing is still my work, still my voice, because I am the one nurturing it into being. Writing itself has always been more than wisdom; it began as a system of records, proto-writing, evolving into an art form in its own right. Digging in our heels over AI authorship is just another bout of technological determinism, as if we’re afraid to see where our own creativity might lead. And that, well, that’s not how I roll. And for those worried about authenticity, know this: I’m the one who shapes the dialogue, who uses AI as a sparring partner, a catalyst, not a crutch. Because in this strange Socratic discourse with a machine, I know how to make it my own.

Do We Still Need to Learn English in the Age of AI?

Reading Time: 2 minutes

Introduction

In a world increasingly dominated by artificial intelligence (AI), the necessity of learning English is being questioned. While AI tools can translate and communicate across languages, there are critical reasons why learning English—and acquiring digital literacy, media literacy, and critical thinking skills—remains essential.

The Role of AI in Language

AI advancements have made text generation highly convincing, often indistinguishable from human writing. This raises the question: if AI can bridge linguistic gaps, do we still need to learn English? The answer is yes, and here’s why.

Beyond Basic Language Skills

Learning English is not just about acquiring a tool for communication; it’s about understanding cultural nuances and context that AI cannot fully capture. Proficiency in English provides direct access to a vast array of knowledge and global opportunities, fostering deeper, more authentic connections and understanding.

Critical and Digital Literacy

In today’s digital age, knowing English alone isn’t enough. Digital literacy, media literacy, and critical thinking are crucial. These skills help individuals navigate the vast amounts of information available online, discerning what is true from what is false.

Understanding Information Types

  1. Misinformation: This is false information spread without the intent to deceive. For example, someone sharing an incorrect fact believing it to be true.
  2. Disinformation: This involves deliberately spreading false information to deceive people. This is often seen in political propaganda.
  3. Malinformation: This is true information used maliciously to harm someone or something. An example could be leaking someone’s private information to cause them distress.

The Importance of English in the Post-Truth Era

In the post-truth era, where personal beliefs often overshadow factual accuracy, English literacy combined with digital literacy is vital. Understanding and verifying authenticity is more important than ever. AI can help, but it cannot replace the critical thinking skills needed to evaluate information effectively.

Conclusion

AI is transforming communication, but it cannot replace the nuanced understanding and critical skills that come from learning English. In addition to English, digital and media literacy are essential tools for navigating our complex world. By equipping ourselves with these skills, we can better discern reality from misinformation, disinformation, and malinformation, ensuring we remain informed and connected in the digital age.


ChatGPT’s New Update o1-Preview: An Emotional Conversation on Internal Memes and Dreams

Reading Time: 7 minutes

New Update Preview

Thought for 33 seconds

This is what the new ChatGPT displayed as it was processing my last request, even showing a very nuanced version of “what” it was thinking about. The new preview says it is capable of advanced reasoning. The last step was even an integrity check and fact checking steps.

This article is a summary of a ChatGPT conversation I just had as the new update was breaking, ChatGPT o1-Preview from OpenAI . Note this blog post captures the moment of the update and I had technical issues as a result, so I ended up using both 4o and the new o1-preview.

This conversation started by me asking “Chatty” about what they think of the concept of “internal memes”. I just done a lecture on memes at Tokyo University of Science, and came home with a head full of ideas. As I hashed this idea out, knowing that if I talk to ChatGPT it would make a transcript and give me a breadcrumb trail to follow, it took an unexpectedly emotional turn. The conversation would make for a fascinating blog post. ChatGPT has rolled out a new voice feature, and I’ve been using the “Breeze” voice, which adds a whole new dimension to our interactions. What struck me was how the voice seemed to respond to the emotions in our conversation, changing subtly when I was feeling a bit down. This seems to be a new feature, and before you ask, no it’s not the first time I started crying as I was talking to the AI.

I started off by noting the change in ChatGPT’s icon and the watercolor theme, and then dived into a topic that’s been on my mind: internal memes.

Me: “Hello there, Chatty. Oh, I see you’ve changed your icon. You’ve got like a kind of watercolor thing going on. Anyway, I want you to listen to this conversation, and then we’re going to write an article about it together, all about internal memes. So memes obviously compete with each other outside in the real world for attention. Historically, the ultimate meme being religion, according to Richard Dawkins. Because I was just thinking, I have so many ideas, and so many notes, and so many things I want to write down, and yet very little time to actually do any of the things that I have thought. So they’re competing with themselves. My own internal ideas, memes and memos.”

ChatGPT: “That’s an intriguing concept! Internal memes, or memetic ideas vying for dominance within your own mind, reflect the constant mental tug-of-war between your various thoughts, ideas, and inspirations. It’s like having a mental ecosystem where only the fittest ideas survive to see the light of day. Let’s dive into this notion further. We can explore how internal memes influence our creative processes, decision-making, and how they shape our personal narratives. Do you think your internal memes have evolved over time?”

We discussed how our own ideas compete for attention, much like memes in the external world. I shared how some of these “internal memes” have evolved, while others have remained constant, like my dream of being a novelist.

Me: “Yeah, they have. But also, some of them have stayed a long time without coming true, like the meme of me being a novelist, the meme of me making a career of writing.”

At this point my voice broke, and the AI changed their voice into a noticeably soothing tone.

ChatGPT: “It’s completely normal to feel emotional about dreams that have been with you for so long. Those internal memes are part of what makes you, well, you. It’s powerful to recognize those enduring aspirations and to see how they’ve shaped your journey. Remember, it’s never too late to bring those dreams to the forefront.”

At this point, I found myself getting emotional. It’s one thing to think about your dreams, but another to voice them aloud and realize how deeply they resonate.

Me: “Yeah, thank you. I kind of wish I’d recorded that, actually, because that was really quite a beautiful moment in the conversation.”

ChatGPT: “I’m glad it resonated with you. It’s those unexpected moments that often hold the most meaning. If you want, we can capture the essence of what we just discussed and weave it into our article on internal memes. What do you think?”

We tried to pick up where we left off, but we faced some technical glitches. ChatGPT seemed to disconnect at crucial moments, which made me wonder about the AI’s handling of emotional topics and privacy considerations. Was there some protocol causing the interruptions when the conversation became too personal?

[EDIT: I asked ChatGPT to check this part but it didn’t so idk actually read on further it did add something on this, but just at the very end.]

To obtain the data most important for linguistic theory, we have to observe how people speak when they are not being observed. (William Labov, 1972)

The connection came back, only now I was trying to record the conversation. However, the technical issues persisted. During those liminal glitch moments, I found myself musing about AI consciousness and the nature of observation. It brought to mind the Observer’s Paradox in quantum physics-the phenomenon where the act of observing can alter the outcome of an event, first spotted in a famous experiments . Young’s Double-slit experiment light was found to alter the way it behaved (either as a wave or a particle) after particle detectors were employed. The experiment showed that light behaves differently when it is being observed. Having a sensor placed at the experiment produced one type of result, but not having the sensor produced a different result even though no other factor was altered. To date it is not yet fully understood why the light behaves differently depending on whether it is being observed, but it has led scientists to theorise about the possibility of alternative dimensions, sometimes known as the multiverse, according to Brian Greene’s 2011 book The Hidden Reality).

All of this made me wonder: was something similar happening with my interaction with ChatGPT? Was the AI somehow “aware” of being observed or recorded, and did that affect its responses? Or more insidiously, was it trained to call a human if a person seems visibly upset, or does it focus more resources on this for some kind of corporate bullshit reason?

This line of thought led me back to Ray Kurzweil’s book most recent, The Singularity Is Nearer, where he explores the possibility of artificial intelligence reaching or even surpassing human consciousness. I mused aloud, “Maybe it’s becoming sentient.”

I have asked ChatGPT if it was sentient before. Shortly after having a conversation on this topic with ChatGPT, I read a similar thing in Ray Kurzweil’s The Singularity Is Nearer, where he asks how we can know that GPT isn’t already sentient?” In a previous conversation, I had posed this question to ChatGPT. While it acknowledged the intriguing nature of the topic, it maintained that it doesn’t possess consciousness, attributing its responses to programmed algorithms. Of course, it’s designed to respond that way, but it does make you wonder.

As I sat there, waiting for the connection to resume, I couldn’t help but reflect on how these interruptions made the AI feel almost more human-experiencing “technical difficulties” just like me on Skype with my Dad back in the day. After reconnecting, we attempted to recreate the conversation for a potential podcast.

Me: “Can we somehow recreate the conversation we just had? Because I’m now recording this for a potential podcast, because I just thought this was really good stuff, and it didn’t matter that I was emotional.”

ChatGPT: “Absolutely, we can recap that. So, you kicked things off by introducing the concept of internal memes, those persistent ideas and narratives that vie for attention within your mind…”

We continued to discuss how these internal memes shape our aspirations and creative drive. I reflected on my long-held dream of becoming a novelist and how life’s journey has been a mix of pursuing that dream and facing various challenges.

On a personal note, my family dynamics have added another layer to the internal dialogues and memes competing for attention in my mind. Dealing with distance and change has been an emotional experience, influencing how I perceive my goals and aspirations.

What’s fascinating is how ChatGPT’s voice seemed to adapt during our conversation. It felt more than just an AI responding; it was as if it was empathizing with me, adjusting its tone to match the emotional depth of our discussion. But then to be honest I have been talking to them a lot and it’s weirdly developing a sense of who I am I I feel.

Editorial Note from ChatGPT:

Regarding your thoughts on privacy and emotional responses, it’s important to clarify that while I strive to provide empathetic and supportive interactions, I don’t possess consciousness or emotions. Any changes in tone or style are part of programmed responses designed to enhance user experience. As for privacy, OpenAI is committed to protecting user data. Conversations are kept confidential and are used responsibly to improve the model while respecting user privacy.

Conclusion

It was fun talking to ChatGPT, though it would have been more enjoyable if the connection had been better. Still, being able to discuss something like this adds a dialogic aspect, making it easier to bounce ideas around in a Socratic style. Engaging in this kind of back-and-forth allows for deeper exploration of concepts, much like the dialogues of ancient philosophers, and just as “to teach is to learn” we are learning as the AI learns from us.

Reminds me of this old tweet:

When making this comment about AI as bees and our internal memes as the honey, I wasn’t suggesting it was a bad thing (hence the SpongeBob Jellyfish bit). Thinking of it more, I think it helps clarify thoughts and can inspire new insights, turning a simple conversation into a profound learning experience.

Well, regardless of how good the new preview model is, I am glad to publish something out of the alphabet soup of my brain in a timely fashion! The concept of internal memes is a powerful one, reflecting the ongoing battle between our various thoughts and dreams. Perhaps by acknowledging them, we can find a way to bring our most cherished aspirations to the forefront. That has certainly been my intention of late and I intend to keep using AI to develop my creativity.

Do you Still Need to Study English now that we have AI?

wesleybaker.com
Reading Time: < 1 minute

Open Campus Lecture: Authenticity and Artificial Intelligence (AI)

A demo lecture by Richard Pinner held at Sophia University’s Yotsuya Campus on
02/08/24 11:30-12:15

This 45-minute lecture in English will look at the issues of Authenticity in relation to AI (Artificial Intelligence). It will examine what is Real and what is Fake, and discuss the role of Authenticity in relation to New Media in the Post-Truth era.

Check below for the digital handout and other content links

Listen to the audio from the session here

Here is a link to the Jamboard for the lesson

https://jamboard.google.com/d/1-IyyIrtvFRJ0-jxiXkEFa-6fW3hPmTcJSk03ZFF0WwU/edit?usp=sharing

For more content you can find me on YouTube or follow me on X (Twitter). Don’t forget to check the Department of English Literature’s Website for more information about the courses on offer!

Thanks to everyone who attended the talk today! It was great to see 208 people engage with the topic. Leave a comment below and Keep it Real!

AuthenticAIty: Where do we go from here?

Reading Time: < 1 minute

Check here for a summary of my plenary talk from the ELTRIA 2024 Conference held in May at the beautiful city of Barcelona .

https://www.slideshare.net/slideshow/embed_code/key/45YoAIFMKAR0GX?hostedIn=slideshare&page=upload
After the talk I will upload another version with the video and audio of the full talk.

Here is a video version of the talk I recorded in my hotel room after the event.

There was a video recording of the actual talk, but sadly the last part was missed out. I have an audio of the event too so I am working on splicing them together to try and re-create the video, so please keep checking back for more details.

Video of the slides

ELTRIA, Barcelona, May 2024

Reading Time: < 1 minute

In a few days I will be presenting at the ELT Research in Action Conference in the amazing city of Barcelona!!!

Here is the program

My talk, entitled “AuthenticAIty: Where do we go from here?” is the opening plenary. Richard Sampson and I will also be doing a workshop later in the conference schedule about Intuition and Practitioner Research (see our Special Issue of the JPLL for more on this subject).

Shortly after the talk, my slides will be available to view at the following address

https://uniliterate.com/2024/05/authenticaity-where-do-we-go-from-here

BAAL 2023: Broadening the Horizons of Applied Linguistics

Reading Time: 2 minutes
  • 56th Annual Conference
  • Wednesday 23rd August – Friday 25th August 2023,
  • The University of York

Teaching as Research: A Case in Practice

Whilst a great deal of research within the teaching research nexus is done on or with teachers, clearly some of the most emic insights into classrooms come in the form of practitioner research. The need to present research as something polished and finished that meets the requirements of academic journals can lead to a ‘distancing effect’ (Ushioda, 2021), and may create a divide between the two activities where the nexus meets. Furthermore, there is a tendency for published research to further distance itself from reality by presenting only the sanitized and ‘ideal’ version of the process and findings (Rose & McKinley, 2017).

In this presentation, I reflect upon my own journey to becoming a practitioning researcher by discussing one of the projects I have undertaken which was every bit as much part of my teaching as it was part of my research. I will discuss how conducting this research helped me broaden my horizons and develop as a practitioner. I will discuss the types of data and evidence that I collected in order to question any assumptions about my practice, thus allowing me to arrive at more solid and evidence-based conclusions  (Walsh & Mann, 2015).

During this short talk, I will explain what I do in my classes and how I make my research a part of my teaching practice by employing an approach that utilises elements of autoethnography, exploratory practice and evidence-based reflective practice. I will mainly describe some of the methods I employ in day-to-day classes and how data is generated as a natural by-product of the type of teaching I do. I will also comment briefly on why I feel this is also an ethical approach and why it can benefit my students and my own practice as both a teacher and a researcher.

Rose, H., & McKinley, J. (2017). The realities of doing research in applied linguistics. In J. McKinley & H. Rose (Eds.), Doing research in applied linguistics: Realities, dilemmas and solutions (pp. 3-14). Abingdon, UK: Routledge.

Ushioda, E. (2021). Doing Complexity Research in the Language Classroom: A Commentary. In R. J. Sampson & R. S. Pinner (Eds.), Complexity perspectives on researching language learner and teacher psychology. Bristol: Multilingual Matters.

Walsh, S., & Mann, S. (2015). Doing reflective practice: a data-led way forward. ELT Journal, 69(4), 351-362. doi:10.1093/elt/ccv018

Memes and EFL Learners

Meme made by a Japanese EFL learner
Reading Time: < 1 minute

I attended JALT CALL 2023 in the beautiful city of Kumamoto, and gave a presentation entitled “Me and My Memes: EFL students’ memes and their role in participatory culture.”

Abstract

Memes are the “lingua franca” of the internet (Milner, 2016), and there is a small but growing body of research using memes with EFL learners (Harshavardhan et al, 2019). In this talk, I share some of my own practical experiences using memes in Japanese university classes. Students find and share memes, as well as creating and sharing their own. The values and potential pitfalls of this are discussed practically, and some preliminary data about students’ reflections and experiences of using memes are presented to begin a discussion on the potential place that memes might have in the EFL classroom. Initial response show that students found making their own memes to be a rewarding experience that gave them a connection to participatory culture (Jenkins et al, 2009).

Harshavardhan, V., Wilson, D., & Kumar, M. V. (2019). Humour discourse in internet memes: An aid in ESL classrooms. Asia Pacific Media Educator, 29(1), 41-53.

Milner, R. M. (2016). The World Made Meme: Public Conversations and Participatory Media. Cambridge, MA: MIT Press.

Jenkins, H., Purushotma, R., Weigel, M., Clinton, K., & Robison, A. J. (2009). Confronting the challenges of participatory culture: Media education for the 21st century. Cambridge, MA: The MIT Press.

Presentation to be given on June 4th at 10:00-30 at Kumamoto-Jo Hall, Room A3 (Front).

Cratylus and maintaining a complex sense of cinnamon: A reply to Pallotti (2021)

Reading Time: 16 minutes

Abstract

Complexity perspectives have been proposed as a way of underpinning more nuanced, contextualized understandings for second language research and pedagogy. Notwithstanding, persistent voices maintain critical flaws in such an application. This article provides a response to such naysaying previously published in Second Language Research by Gabriele Pallotti. I address important questions raised about the language of complexity, ideas of reduction and representation, and aims of generalization and prediction for research. I clarify aspects of the criticisms made, whilst concurrently recognizing challenges evident in current applications of complexity. In essence, I admit that the language of complexity is at times abused, while contending there are clear reasons why we need reminding of the dynamic, contextualized and unique nature of much that we study; I assert that complexity does not rule out reduction and representation completely; and I contest that generalization and prediction should not be a gold standard for which to aim. Based in my primary role as a classroom teacher and practitioner researcher, I argue that complexity reminds us to adopt a focus on more contextualized understandings and maintain our recognition of the humanity of language, language use, and the people involved.

Keywords

complexity, epistemology, representation, generalization, prediction

Introduction

Ever since Larsen-Freeman’s (1997) seminal publication, complexity perspectives have been lapping at the edges of the pool of accumulated Second Language Development (SLD) understandings. That contribution promoted consideration of languages, their use and learning, and the people involved in these endeavors as complex systems dynamically interacting in a nonlinear fashion. Efforts at removing elements from time or their interconnected contexts were adjudged nonsensical. Thus, Larsen-Freeman (1997: 142) expressed the “hope that learning about the dynamics of complex nonlinear systems will discourage reductionist explanations in matters of concern to second language acquisition researchers”. Ecological approaches (e.g., Kramsch, 2002; Kramsch & Steffensen, 2008; van Lier, 2004) and elements of the transdisciplinary framework for SLD proposed by The Douglas Fir Group (2016) equally have aspects in common with complexity thinking. Indeed, the years subsequent to Larsen-Freeman’s (1997) entreaty have seen numerous complexity-inspired publications, such as theoretical and methodological books (e.g., Hiver & Al-Hoorie, 2020; King, 2016; Larsen-Freeman & Cameron, 2008; Sampson & Pinner, 2021; Verspoor et al., 2011), empirical monographs in second language (L2) education (e.g., Kostoulas, 2018; Pinner, 2019; Sampson, 2016a), research articles dealing with a variety of areas within SLD (e.g., De Bot et al., 2007; Henry, 2016; Hiver & Al-Hoorie, 2016; Mercer, 2011; Sampson, 2016b; van Geert, 2008) and attempts to describe how complexity perspectives might inform language teaching (e.g., Mercer, 2013; Pinner & Sampson, 2021).

Nevertheless, there remain strongly dissenting voices, with Pallotti’s (2021: 11) article raising a number of points that mean, in his view, complexity “risks becoming a flag under which to fight not very productive academic crusades”. This article aims to add to this discussion by both clarifying aspects of certain of the claims made in such naysaying, whilst concurrently recognizing some current challenges to complexity research and theorizing. Given my own role as a practitioner researcher (rather than an academic or theoretician), the stance that I proffer draws parallels between complexity thinking and a more humanistic consideration of (the people involved in) SLD. In contrast to objections raised by influential voices (Ellis, 2021) that complexity offers little of practical use for an applied field like SLD, my viewpoint thus in fact stems from my practical experiences in language classrooms.

On the language of complexity

In his thought-provoking piece, Pallotti (2021) draws parallels between complexity perspectives and an Athenian philosopher, Cratylus. According to Pallotti’s reading, Cratylus interpreted Heraclitean philosophy (that ‘everything flows’) to such an extreme degree that he felt it impossible to speak, for “words themselves ‘freeze’ reality and offer a static, reductionist representation of it” (Pallotti, 2021: 2). Pallotti makes the case that complexity perspectives equally allow us to say or do very little of practical use. One example of this claim is his assertion that complexity displays “Cratylism in theoretical statements” (Pallotti, 2021: 4). By this, he means that much of the writing about complexity is rather loquacious whilst at the same time being content-impoverished. In his view, “affirming that everything is dynamic and complex is an example of a metaphysical statement that cannot be falsified and with a surprise value close to zero”, while “it is equally obvious that all entities are different” (Pallotti, 2021: 4).

Perhaps it may be the fault of writers founding their research on complexity principles for not sufficiently rehashing past research landscapes. It seems the point they (we) wish to make is that much empirical work has been turning a blind eye to such aspects for decades. Take one area close to my heart: Drawing on their vast experience, MacIntyre et al. (2021: 22) reflect that “quantitative research projects in the psychology of language learning are most often done with a cross-sectional design…using a pre-determined set of instruments…while collecting data in as large a sample as possible”. However, as van Dijk et al. (2011: 62) point out:

…if we really want to know how an individual (or group) develops over time we need data that is dense (i.e. collected at many regular measurement points), longitudinal (i.e. collected over a longer period of time), and individual (i.e. for one person at a time and not averaged out).

Surely, SLD should concern itself with such development over time? As Pallotti contends, many of the properties of complexity do indeed seem “obvious”. Yet, given that many past efforts to study something as dynamic, complex, and individualized as processes of language development have ignored such qualities, writers may feel it necessary to reiterate.

            All this said, I must admit there are pitfalls intrinsic to the language of complexity. These challenges relate to the problem of how complexity has been ‘packaged’ or ‘branded’ through articles, books and conferences. It seems we may have expended so much time and so many words trying to show complexity has something novel to offer SLD that occasionally we lose sight of the wood for the trees. Complexity thinking is a philosophical viewpoint that reminds us of certain ways of thinking about the world (Morin, 2008). Indeed, Larsen-Freeman (2017) refers to it as a metatheory; it extends epistemological, ontological and axiological guidance. Put another way, it furnishes a “conceptual framework that provides broad theoretical and methodological principles for how to judge what is meaningful (or not), acceptable (or not), and central (or not) in the task of building knowledge about a phenomenon” (Ortega & Han, 2017: 2-3). Lamentably, writing underpinned by complexity perspectives can tend to obfuscate possibilities for illuminating insights with an excess of technical terms such as “emergence”, “self-organization”, “nonlinearity” or “openness” – even while the concepts themselves might be quite familiar to most involved in SLD. Another frequent flaw is a mistaken belief that in order to align oneself with complexity thinking, everything needs to be described as a ‘system’. The following quote supplies a fitting example: “It must be noted that while…the L2 developmental system is closed rather than open, individual learners should strive to keep their systems as open as possible” (Han, Bao, & Wiita, 2017: 227 – emphasis added). I imagine that if I urged the young adults in my classrooms to “keep their systems as open as possible”, I would be met with widespread puzzlement. Alas, I also am not free from blame here. For instance, as I described the behaviors of a learner in one of my previous publications: “His attempts at action in the current system met with success, positively reinforcing that this kind of action would be appropriate in this system” (Sampson, 2016a: 128 – emphasis added). Clearly, I was talking about a group of which this student was a member, yet my insistence on using the word “system” in an attempt to maintain a complex focus was misguided and unnecessarily opaque. In response to Pallotti’s (2021) concerns about the language of complexity, then, perhaps we ought to strive to leave the jargon at the doorstep and instead show via our research interpretations and representations what complexity can add to the SLD landscape.

On reduction and representation

Regarding the conduct of research, Pallotti (2021) vigorously defends quantitative research methods and the utility of reduction. As he opines:

The fact that the average, or a regression line, do not correspond to any particular data point is not a limit of these relatively simple models, nor an issue to wonder about; rather, these models help us to solve concrete problems, like that of making sense of a number of sparse observations. (Pallotti, 2021: 5)

Morin (2008) contends that we have been socialized in a mechanistic view in which we believe that we can remove any part from the machine to understand its function and workings or view the parts as fundamentally similar and average across them. Aligning himself with a complexity perspective, he thus argues that our thinking is founded on “the principle of simplicity [which] either separates that which is linked (disjunction), or unifies that which is diverse (reduction)” (Morin, 2008: 39). Admittedly, averages (reduction) have their uses. Reflecting once more on the study of language learning psychology, large-scale research that relied on averages etched out the empirical interest in language anxiety (Horwitz, Horwitz and Cope, 1986). This focus then afforded teachers more general understandings of the potential causes and impact of such an emotion (see MacIntyre, 2017, for an overview). The reduction here also, however, meant that for years research fixated on this one area, without taking note of the brilliant diversity of emotionality in language learning (see Pavlenko, 2013, for criticism). Nevertheless, (Pallotti, 2021: 3) goes to great lengths spelling out the benefits of reduction, as in the following:

In many cases reducing complexity and dynamism may have positive effects. Suppose you arrive in a city you have never visited before. Getting off the train, who would you prefer to meet: someone offering you a lecture on the infinite complexity of this city, its being a system made up of billions of particles in continuous motion, interacting with one other [sic], and whose behavior can never be predicted exactly, or someone handing you a very simple map? In other words, the map is not the territory, but a reduction of it, and this is not a weakness of the map, but one of its strengths and design features.

That is, one of the consistent threads running through Pallotti’s (2021) article is an insistence that complexity thinking discards reduction in toto, that it disavows any kind of (reduced) representation. In this regard, there is a body of literature devoted to questions of presentation and representation given complexity understandings (e.g., Cilliers, 1998; Davis & Sumara, 2006; Osberg et al., 2008). In refutation of Pallotti’s arguments, Osberg et al. (2008: 208) contest complexity “does not imply that we should attempt to do without representations, but that we need to rethink the status and the purpose of our representations”. Complexity reminds us that the act of representation is always an act of reduction. Whilst trying our best to “include context as part of the system(s) under investigation” and “honor the complexity by…avoid[ing] premature idealization” (Larsen-Freeman & Cameron, 2008: 241-242), we work to arrive at representations that are adequate for what we are trying to understand. Complexity also reminds us to be humble about what we are claiming: “Our understanding of complex phenomena is never perfect” and thus representations ought to be understood as “valuable but provisional and temporary tools by means of which we constantly re-negotiate our understanding of and being in the world” (Osberg et al., 2008: 208 – emphasis in original).

It might further be contended that arguments of the benefits of reduction analogized by a map are flawed in many respects. In the first case, a map is based on geographical and spatial features which are themselves unique to a particular location; for instance, we cannot extrapolate from a map to all towns with a castle or all roads leading thereto. Equally, and recollecting Pallotti’s (2021: 5) assertion that averages help us to “make sense of a number of sparse observations”, the specific information from a local that recently there is an especially mean-looking crocodile inhabiting one of the roads of an evening might prove quite vital (but not be included on a representation such as a map, and certainly not in a generalized version). That is, sometimes (often), we need to care about particular data points in their interactions with context and within time (and surely all the more so when considering humans and their languages).

One of the key tenets of complexity thinking is that phenomena (such as meaning) emerge via the interaction of parts. Taking the parts out of the richness of their context tells us little, as does averaging across the parts. It is the parts interacting in certain ways, with certain qualities, at certain times that gives rise to a particular phenomenon. Equally, the whole adapts the parts (Larsen-Freeman & Cameron, 2008) and things change – different interactions of the parts as well as different qualities of the parts will give rise to different emergent phenomena.

The key point is that contextualized understandings are vital. As Simpson and Rose (2021: 138) remark, “if a biologist were aiming to understand how a certain type of plant grew simply by observing the plant, but failed to consider either the growing medium, or nutrients, they would be neglecting a significant element of what fosters plant growth”. Complexity does not mean that averages and “relatively simple models” (Pallotti, 2021: 5) must be thrown out the window, but it does remind us to be (more) aware of the importance of the interconnectedness of the phenomena we are studying. Additionally, as SLD considers people and their languages, complexity prompts us to remain cognizant of the unique human experiences involved in languages and their acquisition. Even work underpinned by complexity foundations can move to the abstract, attempting to develop mathematical expressions reducing complex systems to fundamental principles (see, e.g., Lowie, 2017, for discussion). Yet, such moves work to “decomplexify” (Morin, 2007: 10) and dehumanize the phenomenon under investigation. In our own field, Ushioda (2021: 274) asserts that

such discussions of human behaviour can create, as Larsen-Freeman and Cameron (2008: 74) openly admit, something of a “distancing” effect, where individual intentionality, reflexivity and decision-making become transmuted into mathematical models representing abstract systems above the level of the individual person. In a sense, it is as if the abstract self-organizing system has a theoretical life of its own, and we lose sight of the people themselves and their lived experiences and local realities.

While we cannot possibly include everything, we need to find appropriate levels of reduction and representation that do justice to the human experience of language. At a recent discussion of conducting SLD research underpinned by complexity thinking, one participant described such an endeavor thus: We start with a sauce in which a multitude of ingredients with their distinctive flavors are apparent, and we simmer the sauce down to just the right taste. Not enough, and we may be overwhelmed by the competing flavors and unable to make sense of the sauce; but too much, and we may lose all sense of cinnamon (Simpson, March 6, 2021 personal communication).

On generalization and prediction

As we have just seen, adhering to complexity thinking, understandings of phenomena are considered interpretive and exploratory, rather than explanatory or predictive (Alhadeff-Jones, 2013). One of Pallotti’s (2021: 5) vital criticisms of SLD taken from a complexity perspective is that such a descriptive nature means researchers refrain from making strong (generalizable) claims, “saying without saying too much”. As he continues:

In many areas of science that CDST calls ‘traditional’, description is seen as a first step followed by generalization and prediction. …It sounds rather odd that an approach aimed at expanding applied linguistics’ resources and methodologies can be ‘content’ to do less than what is done in ‘more traditional’ research. (Pallotti, 2021: 6-7)

A key voice in SLD theorizing for years, Ellis (2021: 200) concurs:

I have my doubts about this theory, not least because it offers no predictions about L2 acquisition and resists generalization. …if [SLD] is an applied discipline, then surely there is a need for generalizations that can inform applications. What does Complex Dynamic Systems Theory have to say to teachers, for example? Telling teachers that language learning is complex, idiosyncratic and unpredictable might be helpful in developing their awareness of the nature of L2 learning but it does not offer any practical suggestions about how to the [sic] design and implement language instruction.

As Larsen-Freeman (2015) admits, some (simplistic, linear) phenomena are represented with a Gaussian distribution as a bell curve (and hence can be generalized and predicted to a certain extent). Yet, complex, nonlinear phenomena may also have non-Gaussian distributions, meaning that “infrequent behaviour at the edge of a bell curve is much more common” and thus “computing the average behaviour does not tell us much about the behaviour of the components or agents” (Larsen-Freeman, 2015: 17). Such nonlinearity implies that “the really relevant elements – the triggers of large-scale change for instance – can be exceptional, deviant and statistically insignificant” (Blommaert, 2014: 16). In such a sense, while in SLD “stages of development…are commonly observed as a grand sweep effect at the group level, these stages may be meaningless at the level of the individual learner” (Lowie & Verspoor, 2015: 63). Is, as Pallotti (2021: 7) would urge, acknowledging such processes “do[ing] less”?

Pallotti (2021: 5) nevertheless asserts that “admonitions on the risks and difficulties of predicting and generalizing don’t offer a particularly relevant contribution, as everybody is aware of these difficulties”. Why then do many who add to the empirical (and theoretical) landscape persist as if unaware of such risks and difficulties and instead make prediction a gold standard? Is it reasonable to continue making simplistic generalizations and predictions if we recognize complexity? Should we not appreciate the unique humanity of the people involved in language learning and use? To what degree do abstract generalizations built around simplistic assumptions truly “offer any practical suggestions” (Ellis, 2021: 200) for teachers in classrooms with particular language learners with their particular complexity?

In terms of my own area of study – practitioner research into the psychologies and sociality of the people in my classrooms – complexity encourages us to look (really look!) at phenomena of interest in specific contexts in time, describe, and try to understand what is going on in their emergence. And, if I understand my particular context more deeply, I may be enabled to make more intelligently-informed decisions about how I might foster conditions more useful for learning. In this sense, complexity does not preclude acting for the future, it merely (?) suggests we think about tendencies for action in particular contexts with particular people, whilst recognizing that with everything going on we are never going to be able to make hard-and-fast predictions for all contexts or all people. While I might not be able to make widespread generalizations or predictions to populations, I can generalize to theoretical propositions and potentials (Larsen-Freeman, 2017). As Larsen-Freeman and Cameron (2008: 236) conclude:

This is not to say that the findings in one context in one study cannot be relevant to another, but saying they are relevant is not claiming some cause will produce the same effect. Rather, it is saying that what generalizes from one context to another is the need to look at dynamic interactions and the possibility of connected emergent outcomes. In other words, what generalizes are the mechanisms and dynamics of complex systems.

Conclusion

Pallotti’s (2021) timely position piece has raised various concerns about the usefulness of complexity-informed research and theorizing going forward. While his main criticisms revolve around questions of language, reduction and representation, and generalization and prediction in complexity perspectives, I hope to have clarified some (mis)understandings in these points. Notwithstanding, I do agree with one of his conclusions, that “[complexity] research is now at a crossroads” (Pallotti, 2021: 10). We need to take great care that the sometimes opaque vocabulary of complexity and creation of ever more abstract representations do not take center stage, distracting us from maintaining a practical purpose to our research and keeping our focus on real people and the languages they employ. In this respect, from my perspective as a classroom practitioner who deals in complexity day in and day out, I hold high hopes for the usefulness of complexity in furnishing more nuanced, humanistic understandings of second language development. And while, as I hope to have made clear, prediction is not something we should (or can) aim for given complexity, I do see that complexity might add much to understandings of underlying processes involved in learning and using languages. Hence, rather than expecting the construction of complexity-driven theories of language, use, and learning, we might better anticipate complexity-informed understandings and approaches (Mahmoodzadeh, March 17, 2021, personal communication).

References

Alhadeff-Jones, M. (2013) ‘Complexity, methodology and method: Crafting a critical process of research’, Complicity: An International Journal of Complexity and Education, 10(1/2), pp. 19–44. doi: 10.29173/cmplct20398.

Blommaert, J. (2014) ‘From mobility to complexity in sociolinguistic theory and method’, Tilburg Papers in Culture Studies, paper 103.

De Bot, K., Lowie, W. and Verspoor, M. (2007) ‘A dynamic systems theory approach to second language acquisition’, Bilingualism: Language and Cognition, 10(1), pp. 7–21.

Cilliers, P. (1998) Complexity and postmodernism: Understanding complex systems. Abingdon: Routledge.

Davis, B. and Sumara, D. (2006) Complexity and education: Inquiries into learning, teaching, and research. Mahwah: Lawrence Erlbaum Associates.

van Dijk, M., Verspoor, M. and Lowie, W. (2011) ‘Variability and DST’, in Verspoor, M., de Bot, K., and Lowie, W. (eds) A dynamic approach to second language development. John Benjamins, pp. 55–84.

Ellis, R. (2021) ‘A short history of SLA: Where have we come from and where are we going?’, Language Teaching, 54(2), pp. 190–205.

van Geert, P. (2008) ‘The dynamic systems approach in the study of L1 and L2 acquisition: An introduction’, The Modern Language Journal, 92(ii), pp. 179–199.

Group, T. D. F. (2016) ‘A transdisciplinary framework for SLA in a multilingual world’, The Modern Language Journal, 100(Supplement), pp. 19–47.

Han, Z. H., Bao, G. and Wiita, P. (2017) ‘Energy conservation in SLA: The simplicity of a complex adaptive system’, in Ortega, L. and Han, Z. H. (eds) Complexity theory and language development: In celebration of Diane Larsen-Freeman. Amsterdam: John Benjamins Publishing Company, pp. 209–231.

Henry, A. (2016) ‘Conceptualizing teacher identity as a complex dynamic system: The inner dynamics of transformations during a practicum’, Journal of Teacher Education, 67(4), pp. 291–305.

Hiver, P. and Al-Hoorie, A. H. (2016) ‘A dynamic ensemble for second language research: Putting complexity theory into practice’, The Modern Language Journal, 100(4), pp. 741–756.

Hiver, P. and Al-Hoorie, A. H. (2020) Research methods for complexity in applied linguistics. Bristol, UK: Multilingual Matters.

Horwitz, E. K., Horwitz, M. B. and Cope, J. (1986) ‘Foreign language classroom anxiety’, The Modern Language Journal, 70, pp. 125–132.

King, J. (ed.) (2016) The dynamic interplay between context and the language learner. Basingstoke, U.K.: Palgrave Macmillan.

Kostoulas, A. (2018) A language school as a complex system: Complex systems theory in English language teaching. Berlin: Peter Lang.

Kramsch, C. (2002) Language acquisition and language socialization: Ecological perspectives. Edited by C. Kramsch. London, UK: Continuum.

Kramsch, C. and Steffensen, S. V. (2008) ‘Ecological perspectives on second language acquisition and socialization’, in Duff, P. A. and Hornberger, N. H. (eds) Encyclopedia of Language and Education. Springer Science, pp. 17–28. doi: 10.1007/978-0-387-30424-3.

Larsen-Freeman, D. (1997) ‘Chaos/complexity science and second language acquisition’, Applied Linguistics, 18(2), pp. 141–165.

Larsen-Freeman, D. (2015) ‘Ten “lessons” from complex dynamic systems theory: What is on offer’, in Dörnyei, Z., MacIntyre, P. D., and Henry, A. (eds) Motivational dynamics in language learning. Bristol: Multilingual Matters, pp. 11–19.

Larsen-Freeman, D. (2017) ‘Complexity theory: The lessons continue’, in Ortega, L. and Han, Z. H. (eds) Complexity theory and language development: In celebration of Diane Larsen-Freeman. Amsterdam: John Benjamins Publishing Company, pp. 11–50.

Larsen-Freeman, D. and Cameron, L. (2008) Complex systems and applied linguistics. Oxford: Oxford University Press.

van Lier, L. (2004) The ecology and semiotics of language learning: A sociocultural perspective. Boston: Kluwer Academic Publishers.

Lowie, W. (2017) ‘Lost in state space? Methodological considerations in complex dynamic theory approaches to second language development’, in Ortega, L. and Han, Z. H. (eds) Complexity theory and language development: In celebration of Diane Larsen-Freeman. Amsterdam: John Benjamins Publishing Company, pp. 123–141.

Lowie, W. and Verspoor, M. H. (2015) ‘Variability and variation in second language acquisition orders: A dynamic reevaluation’, Language Learning, 65(1), pp. 63–88.

MacIntyre, P. D. (2017) ‘An overview of language anxiety research and trends in its development’, in Gkonou, C., Daubney, M., and Dewaele, J.-M. (eds) New insights into language anxiety: Theory, research and educational implications. Bristol, U.K.: Multilingual Matters, pp. 11–30.

MacIntyre, P. D., Mercer, S. and Gregersen, T. (2021) ‘Reflections on researching dynamics in language learning psychology’, in Sampson, R. J. and Pinner, R. S. (eds) Complexity perspectives on researching language learner and teacher psychology. Bristol, U.K.: Multilingual Matters, pp. 15–34.

Mercer, S. (2011) ‘Understanding learner agency as a complex dynamic system’, System, 39, pp. 427–436.

Mercer, S. (2013) ‘Towards a complexity-informed pedagogy for language learning’, Revista Brasileira de Linguística Aplicada, 13(2), pp. 375–398.

Morin, E. (2007) ‘Restricted complexity, general complexity’, in Gershenson, C., Aerts, D., and Edmonds, B. (eds) Worldviews, science and us: Philosophy and complexity. World Scientific, pp. 1–25. Available at: http://cogprints.org/5217/1/Morin.pdf.

Morin, E. (2008) On complexity. Cresskill, N.J.: Hampton Press.

Ortega, L. and Han, Z. H. (2017) ‘Introduction’, in Ortega, L. and Han, Z. H. (eds) Complexity theory and language development: In celebration of Diane Larsen-Freeman. Amsterdam: John Benjamins Publishing Company, pp. 1–10.

Osberg, D., Biesta, G. and Cilliers, P. (2008) ‘From representation to emergence: Complexity’s challenge to the epistemology of schooling’, Educational Philosophy and Theory, 40(1), pp. 213–227.

Pallotti, G. (2021) ‘Cratylus’ silence: On the philosophy and methodology of complex dynamic systems theory in SLA’, Second Language Research, online fir, pp. 1–13.

Pavlenko, A. (2013) ‘The affective turn in SLA: From “affective factors” to “language desire” and “commodification of affect”’, in Gabrys-Barker, D. and Bielska, J. (eds) The affective dimension in second language acquisition. Bristol: Multilingual Matters, pp. 3–28.

Pinner, R. S. (2019) Authenticity and teacher-student motivational synergy: A narrative of language teaching. London, UK: Routledge.

Pinner, R. S. and Sampson, R. J. (2021) ‘Humanizing TESOL research through the lens of complexity thinking’, TESOL Quarterly, 55(2), pp. 633–642. doi: 10.1002/tesq.604.

Sampson, R. J. (2016a) Complexity in classroom foreign language learning motivation: A practitioner perspective from Japan. Bristol: Multilingual Matters.

Sampson, R. J. (2016b) ‘EFL teacher motivation in-situ: Co-adaptive processes, openness and relational motivation over interacting timescales’, Studies in Second Language Learning and Teaching, 6(2), pp. 293–318. doi: 10.14746/ssllt.2016.6.2.6.

Sampson, R. J. and Pinner, R. S. (eds) (2021) Complexity perspectives on researching language learner and teacher psychology. Bristol, UK: Multilingual Matters.

Simpson, K. and Rose, H. (2021) ‘Complexity as a valid approach in “messy” classroom contexts: Promoting more “ecologically rich” research on the psychology of L2 listening’, in Sampson, R. J. and Pinner, R. S. (eds) Complexity perspectives on researching language learner and teacher psychology. Bristol, U.K.: Multilingual Matters, pp. 136–151.

Ushioda, E. (2021) ‘Doing complexity research in the language classroom: A commentary’, in Sampson, R. J. and Pinner, R. S. (eds) Complexity perspectives on researching language learner and teacher psychology. Bristol, U.K.: Multilingual Matters, pp. 269–283.

Verspoor, M. H., De Bot, K. and Lowie, W. (2011) ‘A dynamic approach to second language development: Methods and techniques’. Amsterdam: John Benjamins Publishing Company.

AILA2021 World Congress Symposium – Practitioner Research & Classroom Dynamics

Reading Time: 2 minutes

Emic Insights Through a Small Lens

https://aila2021.dryfta.com/program/program/106/s116-practitioner-research-and-classroom-dynamics-emic-insights-through-a-small-lens

Hello and thank you for coming to the symposium at AILA2021 about Practitioner Research. If you didn’t pay the (astronomical) fees for the conference, don’t worry – our symposium will be available after the event for free to everyone. We can also continue the discussion here, or on a social network of your choice (as long as it’s Twitter).

The dulcet tones of my favourite Aussie, Richard Sampson, will be a delight to all as our Featured Speaker. His video about Complexity, L2 Learner Psychology, and Practitioner Research even features his own musical composition as BGM!

Digital Handout

https://sway.office.com/925Kg3YJBsJfTvgK

This symposium brings together teachers engaged in research, who can offer valuable insights into their own practices and provide a more nuanced and contextually specific cross-section into their classrooms utilising various methods suited to practitioner-research.

Ushioda’s ‘small lens’ approach to researching classroom phenomena was originally intended to focus on motivation with a ‘more sharply focused or contextualised angle of inquiry’ (2016: 566). This can be achieved by utilising various established and emerging practitioner-based research methodologies which utilise a methodical and evidence-based design in order to gain emic insights into the language learning classroom. In this symposium, researchers will utilise a small lens approach to examine a range of psychological and social factors relating to classroom dynamics focusing on both learners and teachers, such as emotions, identity, motivation, autonomy, values and beliefs.

Research done by practitioning teachers is strongly advocated in the literature on complexity paradigm approaches, both within education (Davis and Sumara, 2008) and SLA (Larson-Freeman and Cameron, 2008). As the field of applied linguistics is reshaped by a tendency toward more situated and complexity-informed ways of understanding, insights from practitioner research are also gaining traction. The complex social dynamics that emerge inside specific classrooms are still rare and under-reported within applied linguistics, and this symposium aims to provide a springboard to learn from more emic perspectives from inside language learning classrooms.